The effect of dose and timing of dose on the association between airborne particles and survival

Abstract

Background: Understanding the shape of the concentration-response curve for particles is important for public health, and lack of such understanding was recently cited by U.S. Environmental Protection Agency (EPA) as a reason for not tightening the standards. Similarly, the delay between changes in exposure and changes in health is also important in public health decision making. We addressed these issues using an extended follow-up of the Harvard Six Cities Study.

Methods: Cox proportional hazards models were fit controlling for smoking, body mass index, and other covariates. Two approaches were used. First, we used penalized splines, which fit a flexible functional form to the concentration response to examine its shape, and chose the degrees of freedom for the curve based on Akaike's information criterion. Because the uncertainties around the resultant curve do not reflect the uncertainty in model choice, we also used model averaging as an alternative approach, where multiple models are fit explicitly and averaged, weighted by their probability of being correct given the data. We examined the lag relationship by model averaging across a range of unconstrained distributed lag models.

Results: We found that the concentration-response curve is linear, clearly continuing below the current U.S. standard of 15 microg/m3, and that the effects of changes in exposure on mortality are seen within two years.

Conclusions: Reduction in particle concentrations below U.S. EPA standards would increase life expectancy.

Keywords: PM2.5; air pollution; dose response; model averaging; particles; spline; survival; threshold; uncertainty.

Figure 1

The estimated concentration–response relation between PM_2.5 and the risk of death in the Six Cities Study, using a penalized spline with 18 knots. Also shown are the pointwise 95% CIs.

Figure 2

The estimated concentration–response relation between PM_2.5 and the risk of death in the Six Cities Study, based on averaging the 32 possible models that were fit. Also shown are the point-wise 95% CIs around that curve, based on jacknife estimates.

Figure 3

The estimated concentration–response relation between PM_2.5 and the risk of death in the Six Cities Study, based on averaging the 32 possible models fit under the an uninformative prior, and under a prior giving a linear no-threshold model only half the probability of all other models. There is little difference in the two curves.

Figure 4

The model-averaged estimated effect of a 10-μg/m³ increase in PM_2.5 on all-cause mortality at different lags (in years) between exposure and death. Each lag is estimated independently of the others. Also shown are the pointwise 95% CIs for each lag, based on jacknife estimates.

Figure 5

The model-averaged estimated effect of a 10-μg/m³ increase in PM_2.5 on all-cause mortality and on lung cancer mortality. The estimated effect for lung cancer remains elevated up to 3 years preceding the death. Also shown are the pointwise 95% CIs for each lag, based on jacknife estimates.